We introduce HoVer (HOppy VERification), a dataset for many-hop evidence extraction and fact verification.
Regarding image forensics, researchers have proposed various approaches to detect and/or localize manipulations, such as splices.
Purpose: Using machine learning method to realize automatic severity assessment (non-severe or severe) of COVID-19 based on chest CT images, and to explore the severity-related features from the resulting assessment model.
We propose an innovative method to formulate the issue of localizing manipulated regions in an image as a deep representation learning problem using the Information Bottleneck (IB), which has recently gained popularity as a framework for interpreting deep neural networks.
It comprises a novel approach for learning rich filters and for suppressing image-edges.